Dynamic network slicing for multitenant heterogeneous cloud radio access networks

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants' priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS) and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness and QoS performance compared to several baseline schemes

Quality of Service Aware Dynamic Bandwidth Allocation for Rate Control in WSN

Different types of data can be generated by Wireless Sensor Networks (WSNs) in both Real-Time (RT) and Non-RT (NRT) scenarios. The combination of these factors, along with the limited bandwidth available, necessitates careful management of these categories in order to reduce congestion. Due to this, a Proficient Rate Control  and Fair Bandwidth Allocation (PRC-FBA) method has been created that prioritizes certain types of traffic and creates a virtual queue for them.In PRC-FBA, the Signal-to-Noise and Interference Ratio (SINR) model is applied to the problem of bandwidth allocation in WSN in an effort to find a compromise between equity and performance. Then, a brand-new bandwidth utility factor is defined with regard to equity and effectivenes. The FBA method in PRC-FBA is devoped for only improving   throughput, but not considering  delay. However, delay is the main factors for trasnmiitng NRT packets.  This paper offers a PRC with Quality of Service (QoS) aware Dynamic Bandwidth Allocation (PRC-QDBA) approach for allocating bandwidth while prioritizing packets based on their traffic classes. This model employs a QoS associated dynamic bandwidth allocation strategy which efficiently distributes the unused time slots among the required nodes. The distribution technique is performed based on hierarchical manner utilizing a parent-child association of tree topology. The parent node receives traffic indication maps (TIMs) from the children nodes and adopts them to allocate time slots based on their demamds. If the parent node is unable to allocate the required slots, it creates a TIM that indicating the demands and transfer it to its immediate parent node. This increases the entire performance rate of RT traffic. Furthermore, this model assures the packet forwarding for previously accepted flows by allowing node transmission based on ancestral connection capabilities. Finally, simulation results demonstartes that the suggested model significantly increases the throughput and delay for bandwidth allocation while also enabling QoS support for RT traffic in WSNs.&nbsp

Interference-aware energy efficiency maximization in 5G ultra-dense networks

Ultra-dense networks can further improve the spectrum efficiency (SE) and the energy efficiency (EE). However, the interference avoidance and the green design are becoming more complex due to the intrinsic densification and scalability. It is known that the much denser small cells are deployed, the more cooperation opportunities exist among them. In this work, we characterize the cooperative behaviors in the Nash bargaining cooperative game-theoretic framework, where we maximize the EE performance with a certain sacrifice of SE performance. We first analyze the relationship between the EE and the SE, based on which we formulate the Nash-product EE maximization problem.We achieve the closed-form sub-optimal SE equilibria to maximize the EE performance with and without the minimum SE constraints. We finally propose a CE2MG algorithm, and numerical results verify the improved EE and fairness of the presented CE2MG algorithm compared with the non-cooperative scheme

TCP Timeout Mechanism for Optimization of Network Fairness and Performance in Multi-Hop Pipeline Network

In the recent years, wireless sensor network (WSN) has a huge impact in many remote based applications especially in oil and gas pipeline monitoring. Thus, the deployment of a multi-hop linear WSN will be a practical solution on pipelines. With a large multi-hop linear WSN, the overall network performance is badly affected especially due to node starvation. Inequality among source nodes is relatively an amplified factor of generated data rate and source node distances from the destination node. In this paper, we proposed a mathematical model for TCP Delayed Timeout Acknowledgement (DTO-ACK) mechanism for non-zero passive nodes. Optimum throughput fairness can be achieved by implementation of DTO-ACK with Dual Interleaving Linear Static Routing Protocol (DI-LSRP) for flat topology. Implementation of DTO-ACK and modification of TCP parameters reduces packet collision and ensures optimal throughput fairness in a multi-hop linear topology using TCP agent

Generalized proportional fair (GPF) scheduler for LTE-A

The growth of wireless traffic and the demand for higher data rates motivated researchers around the world to enhance the Long Term Evolution-Advanced (LTE-A) performance. Recently, a considerable amount of the research had been done to optimise the packet schedulers. The packet schedulers distribute the radio resources among users to increase spectrum efficiency and network performance. In this paper, a Generalized Proportional Fair (GPF) scheduler is used to enhance the scheduler performance compared to the other conventional schedulers. The GPF scheduler performance is compared in terms of users’ throughput, energy efficiency, spectral efficiency and fairness using system level simulations. The simulation results show that the proposed scheduler outperforms the conventional schedulers proposed for LTE-A

Fair Resource Allocation in Macroscopic Evacuation Planning Using Mathematical Programming: Modeling and Optimization

Evacuation is essential in the case of natural and manmade disasters such as hurricanes, nuclear disasters, fire accidents, and terrorism epidemics. Random evacuation plans can increase risks and incur more losses. Hence, numerous simulation and mathematical programming models have been developed over the past few decades to help transportation planners make decisions to reduce costs and protect lives. However, the dynamic transportation process is inherently complex. Thus, modeling this process can be challenging and computationally demanding. The objective of this dissertation is to build a balanced model that reflects the realism of the dynamic transportation process and still be computationally tractable to be implemented in reality by the decision-makers. On the other hand, the users of the transportation network require reasonable travel time within the network to reach their destinations. This dissertation introduces a novel framework in the fields of fairness in network optimization and evacuation to provide better insight into the evacuation process and assist with decision making. The user of the transportation network is a critical element in this research. Thus, fairness and efficiency are the two primary objectives addressed in the work by considering the limited capacity of roads of the transportation network. Specifically, an approximation approach to the max-min fairness (MMF) problem is presented that provides lower computational time and high-quality output compared to the original algorithm. In addition, a new algorithm is developed to find the MMF resource allocation output in nonconvex structure problems. MMF is the fairness policy used in this research since it considers fairness and efficiency and gives priority to fairness. In addition, a new dynamic evacuation modeling approach is introduced that is capable of reporting more information about the evacuees compared to the conventional evacuation models such as their travel time, evacuation time, and departure time. Thus, the contribution of this dissertation is in the two areas of fairness and evacuation. The first part of the contribution of this dissertation is in the field of fairness. The objective in MMF is to allocate resources fairly among multiple demands given limited resources while utilizing the resources for higher efficiency. Fairness and efficiency are contradicting objectives, so they are translated into a bi-objective mathematical programming model and solved using the ϵ-constraint method, introduced by Vira and Haimes (1983). Although the solution is an approximation to the MMF, the model produces quality solutions, when ϵ is properly selected, in less computational time compared to the progressive-filling algorithm (PFA). In addition, a new algorithm is developed in this research called the θ progressive-filling algorithm that finds the MMF in resource allocation for general problems and works on problems with the nonconvex structure problems. The second part of the contribution is in evacuation modeling. The common dynamic evacuation models lack a piece of essential information for achieving fairness, which is the time each evacuee or group of evacuees spend in the network. Most evacuation models compute the total time for all evacuees to move from the endangered zone to the safe destination. Lack of information about the users of the transportation network is the motivation to develop a new optimization model that reports more information about the users of the network. The model finds the travel time, evacuation time, departure time, and the route selected for each group of evacuees. Given that the travel time function is a non-linear convex function of the traffic volume, the function is linearized through a piecewise linear approximation. The developed model is a mixed-integer linear programming (MILP) model with high complexity. Hence, the model is not capable of solving large scale problems. The complexity of the model was reduced by introducing a linear programming (LP) version of the full model. The complexity is significantly reduced while maintaining the exact output. In addition, the new θ-progressive-filling algorithm was implemented on the evacuation model to find a fair and efficient evacuation plan. The algorithm is also used to identify the optimal routes in the transportation network. Moreover, the robustness of the evacuation model was tested against demand uncertainty to observe the model behavior when the demand is uncertain. Finally, the robustness of the model is tested when the traffic flow is uncontrolled. In this case, the model's only decision is to distribute the evacuees on routes and has no control over the departure time

Analytical characterization of inband and outband D2D Communications for network access

Mención Internacional en el título de doctorCooperative short-range communication schemes provide powerful tools to solve interference and resource shortage problems in wireless access networks. With such schemes, a mobile node with excellent cellular connectivity can momentarily accept to relay traffic for its neighbors experiencing poor radio conditions and use Device-to-Device (D2D) communications to accomplish the task. This thesis provides a novel and comprehensive analytical framework that allows evaluating the effects of D2D communications in access networks in terms of spectrum and energy efficiency. The analysis covers the cases in which D2D communications use the same bandwidth of legacy cellular users (in-band D2D) or a different one (out-band D2D) and leverages on the characterization of underlying queueing systems and protocols to capture the complex intertwining of short-range and legacy WiFi and cellular communications. The analysis also unveils how D2D affects the use and scope of other optimization techniques used for, e.g., interference coordination and fairness in resource distribution. Indeed, characterizing the performance of D2D-enabled wireless access networks plays an essential role in the optimization of system operation and, as a consequence, permits to assess the general applicability of D2D solutions. With such characterization, we were able to design several mechanisms that improve system capabilities. Specifically, we propose bandwidth resource management techniques for controlling interference when cellular users and D2D pairs share the same spectrum, we design advanced and energy-aware access selection mechanisms, we show how to adopt D2D communications in conjunction with interference coordination schemes to achieve high and fair throughputs, and we discuss on end-to-end fairness—beyond the use of access network resources—when D2D communications is adopted in C-RAN. The results reported in this thesis show that identifying performance bottlenecks is key to properly control network operation, and, interestingly, bottlenecks may not be represented just by wireless resources when end-to-end fairness is of concern.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Marco Ajmone Marsan.- Secretario: Miquel Payaró Llisterri.- Vocal: Omer Gurewit

Advances in electric power systems : robustness, adaptability, and fairness

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 151-157).The electricity industry has been experiencing fundamental changes over the past decade. Two of the arguably most significant driving forces are the integration of renewable energy resources into the electric power system and the creation of the deregulated electricity markets. Many new challenges arise. In this thesis, we focus on two important ones: How to reliably operate the power system under high penetration of intermittent and uncertain renewable resources and uncertain demand: and how to design an electricity market that considers both efficiency and fairness. We present some new advances in these directions. In the first part of the thesis, we focus on the first issue in the context of the unit commitment (UC) problem, one of the most critical daily operations of an electric power system. Unit commitment in large scale power systems faces new challenges of increasing uncertainty from both generation and load. We propose an adaptive robust model for the security constrained unit commitment problem in the presence of nodal net load uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and outer approximation techniques. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England (ISO-NE). Computational results demonstrate the advantages of the robust model over the traditional reserve adjustment approach in terms of economic efficiency, operational reliability, and robustness to uncertain distributions. In the second part of the thesis, we are concerned with a geometric characterization of the performance of adaptive robust solutions in a multi-stage stochastic optimization problem. We study the notion of finite adaptability in a general setting of multi-stage stochastic and adaptive optimization. We show a significant role that geometric properties of uncertainty sets, such as symmetry, play in determining the power of robust and finitely adaptable solutions. We show that a class of finitely adaptable solutions is a good approximation for both the multi-stage stochastic as well as the adaptive optimization problem. To the best of our knowledge, these are the first approximation results for multi-stage problems in such generality. Moreover, the results and the proof techniques are quite general and extend to include important constraints such as integrality and linear conic constraints. In the third part of the thesis, we focus on how to design an auction and pricing scheme for the day-ahead electricity market that achieves both economic efficiency and fairness. The work is motivated by two outstanding problems in the current practice - the uplift problem and equitable selection problem. The uplift problem is that the electricity payment determined by the electricity price cannot fully recover the production cost (especially the fixed cost) of some committed generators, and therefore the ISOs make side payments to such generators to make up the loss. The equitable selection problem is how to achieve fairness and integrity of the day-ahead auction in choosing from multiple (near) optimal solutions. We offer a new perspective and propose a family of fairness based auction and pricing schemes that resolve these two problems. We present numerical test result using ISO-NE's day-ahead market data. The proposed auction- pricing schemes produce a frontier plot of efficiency versus fairness, which can be used as a vaulable decision tool for the system operation.by Xu Andy Sun.Ph.D